2015/11/26 by Amina Kemmar, Samir Loudni, Kemmar, Amina +7
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1511.08350
openalex publication_date 2015/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity. The Constraint Programming (CP) approaches are not so effective because of the size of their encodings. In[7], we have proposed the global constraint Prefix-Projection for SPM which remedies to this drawback. However, this global constraint cannot be directly extended to support gap constraint. In this paper, we propose the global constraint GAP-SEQ enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the principle of right pattern extensions. Experiments show that our approach clearly outperforms both CP approaches and the state-of-the-art cSpade method on large datasets.